Overview

Dataset statistics

Number of variables19
Number of observations847
Missing cells101
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory125.9 KiB
Average record size in memory152.2 B

Variable types

Categorical9
Numeric10

Alerts

currency has constant value "INR" Constant
title has a high cardinality: 752 distinct values High cardinality
url has a high cardinality: 847 distinct values High cardinality
product_id has a high cardinality: 847 distinct values High cardinality
listing_id has a high cardinality: 847 distinct values High cardinality
highlights has a high cardinality: 489 distinct values High cardinality
selling_price is highly correlated with original_price and 1 other fieldsHigh correlation
original_price is highly correlated with selling_priceHigh correlation
avg_rating is highly correlated with selling_priceHigh correlation
ratings_count is highly correlated with reviews_count and 5 other fieldsHigh correlation
reviews_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
one_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
two_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
three_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
four_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
five_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
selling_price is highly correlated with original_priceHigh correlation
original_price is highly correlated with selling_priceHigh correlation
ratings_count is highly correlated with reviews_count and 5 other fieldsHigh correlation
reviews_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
one_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
two_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
three_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
four_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
five_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
selling_price is highly correlated with original_priceHigh correlation
original_price is highly correlated with selling_priceHigh correlation
ratings_count is highly correlated with reviews_count and 5 other fieldsHigh correlation
reviews_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
one_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
two_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
three_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
four_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
five_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
currency is highly correlated with brand and 2 other fieldsHigh correlation
brand is highly correlated with currencyHigh correlation
availability is highly correlated with currencyHigh correlation
ram is highly correlated with currencyHigh correlation
ram is highly correlated with brand and 1 other fieldsHigh correlation
brand is highly correlated with ram and 3 other fieldsHigh correlation
selling_price is highly correlated with brand and 2 other fieldsHigh correlation
original_price is highly correlated with ram and 2 other fieldsHigh correlation
avg_rating is highly correlated with brand and 1 other fieldsHigh correlation
ratings_count is highly correlated with reviews_count and 5 other fieldsHigh correlation
reviews_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
one_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
two_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
three_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
four_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
five_stars_count is highly correlated with ratings_count and 5 other fieldsHigh correlation
ram has 101 (11.9%) missing values Missing
title is uniformly distributed Uniform
url is uniformly distributed Uniform
product_id is uniformly distributed Uniform
listing_id is uniformly distributed Uniform
highlights is uniformly distributed Uniform
url has unique values Unique
product_id has unique values Unique
listing_id has unique values Unique
avg_rating has 22 (2.6%) zeros Zeros
ratings_count has 22 (2.6%) zeros Zeros
reviews_count has 32 (3.8%) zeros Zeros
one_stars_count has 39 (4.6%) zeros Zeros
two_stars_count has 44 (5.2%) zeros Zeros
three_stars_count has 34 (4.0%) zeros Zeros
four_stars_count has 33 (3.9%) zeros Zeros
five_stars_count has 22 (2.6%) zeros Zeros

Reproduction

Analysis started2022-08-13 09:30:21.287360
Analysis finished2022-08-13 09:32:05.526817
Duration1 minute and 44.24 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

title
Categorical

HIGH CARDINALITY
UNIFORM

Distinct752
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
REDMI Note 10S (Deep Sea Blue, 128 GB)
 
4
REDMI Note 10 Pro (Vintage Bronze, 128 GB)
 
3
REDMI Note 10 Pro (Dark Night, 128 GB)
 
3
vivo T1 5G (Rainbow Fantasy, 128 GB)
 
3
REDMI Note 10S (Cosmic Purple, 128 GB)
 
3
Other values (747)
831 

Length

Max length58
Median length52
Mean length35.45336482
Min length21

Characters and Unicode

Total characters30029
Distinct characters72
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique667 ?
Unique (%)78.7%

Sample

1st rowPOCO C31 (Royal Blue, 64 GB)
2nd rowPOCO C31 (Shadow Gray, 64 GB)
3rd rowrealme C35 (Glowing Green, 64 GB)
4th rowOPPO K10 (Black Carbon, 128 GB)
5th rowMOTOROLA G60 (Soft Silver, 128 GB)

Common Values

ValueCountFrequency (%)
REDMI Note 10S (Deep Sea Blue, 128 GB)4
 
0.5%
REDMI Note 10 Pro (Vintage Bronze, 128 GB)3
 
0.4%
REDMI Note 10 Pro (Dark Night, 128 GB)3
 
0.4%
vivo T1 5G (Rainbow Fantasy, 128 GB)3
 
0.4%
REDMI Note 10S (Cosmic Purple, 128 GB)3
 
0.4%
SAMSUNG Galaxy A12 (Black, 128 GB)3
 
0.4%
vivo T1 5G (Starlight Black, 128 GB)3
 
0.4%
REDMI Note 10S (Frost White, 128 GB)3
 
0.4%
SAMSUNG Galaxy A12 (Blue, 128 GB)3
 
0.4%
REDMI Note 10T 5G (Graphite Black, 128 GB)2
 
0.2%
Other values (742)817
96.5%

Length

2022-08-13T15:02:06.326136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gb839
 
14.4%
128369
 
6.3%
64240
 
4.1%
blue218
 
3.7%
black212
 
3.6%
5g146
 
2.5%
pro142
 
2.4%
32135
 
2.3%
redmi131
 
2.3%
realme114
 
2.0%
Other values (540)3275
56.3%

Most occurring characters

ValueCountFrequency (%)
4974
 
16.6%
e1557
 
5.2%
G1452
 
4.8%
B1303
 
4.3%
a1151
 
3.8%
l1031
 
3.4%
i1014
 
3.4%
o929
 
3.1%
r913
 
3.0%
)867
 
2.9%
Other values (62)14838
49.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11184
37.2%
Uppercase Letter7581
25.2%
Space Separator4974
16.6%
Decimal Number3678
 
12.2%
Close Punctuation867
 
2.9%
Open Punctuation867
 
2.9%
Other Punctuation851
 
2.8%
Math Symbol11
 
< 0.1%
Dash Punctuation7
 
< 0.1%
Other Symbol5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1557
13.9%
a1151
10.3%
l1031
9.2%
i1014
 
9.1%
o929
 
8.3%
r913
 
8.2%
n681
 
6.1%
t564
 
5.0%
c420
 
3.8%
u383
 
3.4%
Other values (16)2541
22.7%
Uppercase Letter
ValueCountFrequency (%)
G1452
19.2%
B1303
17.2%
P752
9.9%
M495
 
6.5%
S495
 
6.5%
A481
 
6.3%
O337
 
4.4%
E312
 
4.1%
N292
 
3.9%
R255
 
3.4%
Other values (16)1407
18.6%
Decimal Number
ValueCountFrequency (%)
1849
23.1%
2836
22.7%
8424
11.5%
6352
9.6%
5330
 
9.0%
3299
 
8.1%
4283
 
7.7%
0177
 
4.8%
985
 
2.3%
743
 
1.2%
Other Punctuation
ValueCountFrequency (%)
,848
99.6%
.3
 
0.4%
Math Symbol
ValueCountFrequency (%)
+9
81.8%
|2
 
18.2%
Space Separator
ValueCountFrequency (%)
4974
100.0%
Close Punctuation
ValueCountFrequency (%)
)867
100.0%
Open Punctuation
ValueCountFrequency (%)
(867
100.0%
Dash Punctuation
ValueCountFrequency (%)
-7
100.0%
Other Symbol
ValueCountFrequency (%)
°5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18765
62.5%
Common11264
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1557
 
8.3%
G1452
 
7.7%
B1303
 
6.9%
a1151
 
6.1%
l1031
 
5.5%
i1014
 
5.4%
o929
 
5.0%
r913
 
4.9%
P752
 
4.0%
n681
 
3.6%
Other values (42)7982
42.5%
Common
ValueCountFrequency (%)
4974
44.2%
)867
 
7.7%
(867
 
7.7%
1849
 
7.5%
,848
 
7.5%
2836
 
7.4%
8424
 
3.8%
6352
 
3.1%
5330
 
2.9%
3299
 
2.7%
Other values (10)618
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII30024
> 99.9%
None5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4974
 
16.6%
e1557
 
5.2%
G1452
 
4.8%
B1303
 
4.3%
a1151
 
3.8%
l1031
 
3.4%
i1014
 
3.4%
o929
 
3.1%
r913
 
3.0%
)867
 
2.9%
Other values (61)14833
49.4%
None
ValueCountFrequency (%)
°5
100.0%

ram
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)0.9%
Missing101
Missing (%)11.9%
Memory size6.7 KiB
4 GB RAM
211 
6 GB RAM
183 
8 GB RAM
158 
3 GB RAM
87 
2 GB RAM
81 
Other values (2)
26 

Length

Max length9
Median length8
Mean length8.018766756
Min length8

Characters and Unicode

Total characters5982
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4 GB RAM
2nd row4 GB RAM
3rd row4 GB RAM
4th row6 GB RAM
5th row6 GB RAM

Common Values

ValueCountFrequency (%)
4 GB RAM211
24.9%
6 GB RAM183
21.6%
8 GB RAM158
18.7%
3 GB RAM87
10.3%
2 GB RAM81
 
9.6%
12 GB RAM14
 
1.7%
1 GB RAM12
 
1.4%
(Missing)101
11.9%

Length

2022-08-13T15:02:06.513629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-13T15:02:06.857260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
gb746
33.3%
ram746
33.3%
4211
 
9.4%
6183
 
8.2%
8158
 
7.1%
387
 
3.9%
281
 
3.6%
1214
 
0.6%
112
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1492
24.9%
G746
12.5%
B746
12.5%
R746
12.5%
A746
12.5%
M746
12.5%
4211
 
3.5%
6183
 
3.1%
8158
 
2.6%
295
 
1.6%
Other values (2)113
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3730
62.4%
Space Separator1492
 
24.9%
Decimal Number760
 
12.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4211
27.8%
6183
24.1%
8158
20.8%
295
12.5%
387
11.4%
126
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
G746
20.0%
B746
20.0%
R746
20.0%
A746
20.0%
M746
20.0%
Space Separator
ValueCountFrequency (%)
1492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3730
62.4%
Common2252
37.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1492
66.3%
4211
 
9.4%
6183
 
8.1%
8158
 
7.0%
295
 
4.2%
387
 
3.9%
126
 
1.2%
Latin
ValueCountFrequency (%)
G746
20.0%
B746
20.0%
R746
20.0%
A746
20.0%
M746
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1492
24.9%
G746
12.5%
B746
12.5%
R746
12.5%
A746
12.5%
M746
12.5%
4211
 
3.5%
6183
 
3.1%
8158
 
2.6%
295
 
1.6%
Other values (2)113
 
1.9%

brand
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
realme
114 
SAMSUNG
109 
APPLE
101 
REDMI
83 
Mi
75 
Other values (22)
365 

Length

Max length16
Median length9
Mean length5.18772137
Min length2

Characters and Unicode

Total characters4394
Distinct characters42
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.4%

Sample

1st rowPOCO
2nd rowPOCO
3rd rowrealme
4th rowOPPO
5th rowMOTOROLA

Common Values

ValueCountFrequency (%)
realme114
13.5%
SAMSUNG109
12.9%
APPLE101
11.9%
REDMI83
9.8%
Mi75
8.9%
OPPO67
7.9%
vivo64
7.6%
Infinix44
 
5.2%
Tecno36
 
4.3%
POCO33
 
3.9%
Other values (17)121
14.3%

Length

2022-08-13T15:02:07.044756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
realme114
13.4%
samsung109
12.8%
apple101
11.9%
redmi83
9.8%
mi75
8.8%
oppo67
7.9%
vivo64
7.5%
infinix44
 
5.2%
tecno36
 
4.2%
poco33
 
3.9%
Other values (19)125
14.7%

Most occurring characters

ValueCountFrequency (%)
P371
 
8.4%
M315
 
7.2%
O293
 
6.7%
e290
 
6.6%
A262
 
6.0%
i261
 
5.9%
S230
 
5.2%
E204
 
4.6%
I160
 
3.6%
a160
 
3.6%
Other values (32)1848
42.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2655
60.4%
Lowercase Letter1735
39.5%
Space Separator4
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P371
14.0%
M315
11.9%
O293
11.0%
A262
9.9%
S230
8.7%
E204
7.7%
I160
 
6.0%
L138
 
5.2%
N131
 
4.9%
G119
 
4.5%
Other values (13)432
16.3%
Lowercase Letter
ValueCountFrequency (%)
e290
16.7%
i261
15.0%
a160
9.2%
l158
9.1%
n137
7.9%
o131
7.6%
r129
7.4%
v128
7.4%
m126
7.3%
x56
 
3.2%
Other values (8)159
9.2%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4390
99.9%
Common4
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
P371
 
8.5%
M315
 
7.2%
O293
 
6.7%
e290
 
6.6%
A262
 
6.0%
i261
 
5.9%
S230
 
5.2%
E204
 
4.6%
I160
 
3.6%
a160
 
3.6%
Other values (31)1844
42.0%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P371
 
8.4%
M315
 
7.2%
O293
 
6.7%
e290
 
6.6%
A262
 
6.0%
i261
 
5.9%
S230
 
5.2%
E204
 
4.6%
I160
 
3.6%
a160
 
3.6%
Other values (32)1848
42.1%

url
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct847
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
https://www.flipkart.com/poco-c31-royal-blue-64-gb/p/itm19effae969b86?pid=MOBG73E7GKQK4KZP
 
1
https://www.flipkart.com/realme-7-mist-white-128-gb/p/itme55d08631f19b?pid=MOBFUYUNHENTTY9M
 
1
https://www.flipkart.com/apple-iphone-13-pro-graphite-256-gb/p/itm1b16c87a4a64a?pid=MOBG6VF5WKMAADTZ
 
1
https://www.flipkart.com/xiaomi-11t-pro-5g-hyperphone-moonlight-white-256-gb/p/itmc9e9597986ec5?pid=MOBGB74URFDJ3EGH
 
1
https://www.flipkart.com/oppo-f9-pro-starry-purple-64-gb/p/itmf8fczgnrn5xhy?pid=MOBF8FCZHK2HWJVT
 
1
Other values (842)
842 

Length

Max length116
Median length108
Mean length97.18181818
Min length83

Characters and Unicode

Total characters82313
Distinct characters66
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique847 ?
Unique (%)100.0%

Sample

1st rowhttps://www.flipkart.com/poco-c31-royal-blue-64-gb/p/itm19effae969b86?pid=MOBG73E7GKQK4KZP
2nd rowhttps://www.flipkart.com/poco-c31-shadow-gray-64-gb/p/itm162375acb8370?pid=MOBG73E7UBFXXMCH
3rd rowhttps://www.flipkart.com/realme-c35-glowing-green-64-gb/p/itmafb045222b2cf?pid=MOBGBTHFSKHF8RAU
4th rowhttps://www.flipkart.com/oppo-k10-black-carbon-128-gb/p/itm6205e7e72fe0c?pid=MOBGCFUHMDFSCM9W
5th rowhttps://www.flipkart.com/motorola-g60-soft-silver-128-gb/p/itm7d158ff189510?pid=MOBG9CJ6G5GCFAH4

Common Values

ValueCountFrequency (%)
https://www.flipkart.com/poco-c31-royal-blue-64-gb/p/itm19effae969b86?pid=MOBG73E7GKQK4KZP1
 
0.1%
https://www.flipkart.com/realme-7-mist-white-128-gb/p/itme55d08631f19b?pid=MOBFUYUNHENTTY9M1
 
0.1%
https://www.flipkart.com/apple-iphone-13-pro-graphite-256-gb/p/itm1b16c87a4a64a?pid=MOBG6VF5WKMAADTZ1
 
0.1%
https://www.flipkart.com/xiaomi-11t-pro-5g-hyperphone-moonlight-white-256-gb/p/itmc9e9597986ec5?pid=MOBGB74URFDJ3EGH1
 
0.1%
https://www.flipkart.com/oppo-f9-pro-starry-purple-64-gb/p/itmf8fczgnrn5xhy?pid=MOBF8FCZHK2HWJVT1
 
0.1%
https://www.flipkart.com/motorola-g40-fusion-frosted-champagne-64-gb/p/itmaa22c25971ee0?pid=MOBFWSF8KRVUGH9W1
 
0.1%
https://www.flipkart.com/vivo-y51a-crystal-symphony-128-gb/p/itm72ed89f10c598?pid=MOBFZ2U4GURCS37U1
 
0.1%
https://www.flipkart.com/oppo-f19-pro-fantastic-purple-256-gb/p/itmf3153ba8dbf1a?pid=MOBGYV9VUGTPFWRY1
 
0.1%
https://www.flipkart.com/samsung-galaxy-m11-metallic-blue-64-gb/p/itm1a7c4dfe52d9c?pid=MOBFRZZHZGSRRWUZ1
 
0.1%
https://www.flipkart.com/apple-iphone-se-3rd-gen-midnight-256-gb/p/itm06824d5671624?pid=MOBGC9K3EMZYFNDT1
 
0.1%
Other values (837)837
98.8%

Length

2022-08-13T15:02:07.216553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.flipkart.com/poco-c31-royal-blue-64-gb/p/itm19effae969b86?pid=mobg73e7gkqk4kzp1
 
0.1%
https://www.flipkart.com/oppo-a54-starry-blue-128-gb/p/itm2c71eaacf710e?pid=mobg23kttgynzfrm1
 
0.1%
https://www.flipkart.com/realme-9i-prism-black-128-gb/p/itm3e9987219f652?pid=mobgbmzh9x7pqzjz1
 
0.1%
https://www.flipkart.com/realme-c35-glowing-green-64-gb/p/itmafb045222b2cf?pid=mobgbthfskhf8rau1
 
0.1%
https://www.flipkart.com/oppo-k10-black-carbon-128-gb/p/itm6205e7e72fe0c?pid=mobgcfuhmdfscm9w1
 
0.1%
https://www.flipkart.com/motorola-g60-soft-silver-128-gb/p/itm7d158ff189510?pid=mobg9cj6g5gcfah41
 
0.1%
https://www.flipkart.com/realme-c20-cool-grey-32-gb/p/itmea1903897436b?pid=mobgf489sqzcfhya1
 
0.1%
https://www.flipkart.com/realme-c20-cool-blue-32-gb/p/itmea1903897436b?pid=mobgf4894mewzjgv1
 
0.1%
https://www.flipkart.com/poco-c31-royal-blue-32-gb/p/itm9656ebd21e0b3?pid=mobg73e7zbhhq3rb1
 
0.1%
https://www.flipkart.com/realme-9i-prism-blue-64-gb/p/itm3e9987219f652?pid=mobg9vgvkum4xnrr1
 
0.1%
Other values (837)837
98.8%

Most occurring characters

ValueCountFrequency (%)
-4958
 
6.0%
p4343
 
5.3%
/4235
 
5.1%
t4109
 
5.0%
i3718
 
4.5%
a3138
 
3.8%
w2709
 
3.3%
e2558
 
3.1%
m2541
 
3.1%
c2114
 
2.6%
Other values (56)47890
58.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter45202
54.9%
Decimal Number12019
 
14.6%
Uppercase Letter11664
 
14.2%
Other Punctuation7623
 
9.3%
Dash Punctuation4958
 
6.0%
Math Symbol847
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p4343
 
9.6%
t4109
 
9.1%
i3718
 
8.2%
a3138
 
6.9%
w2709
 
6.0%
e2558
 
5.7%
m2541
 
5.6%
c2114
 
4.7%
o2096
 
4.6%
l2056
 
4.5%
Other values (16)15820
35.0%
Uppercase Letter
ValueCountFrequency (%)
G1271
 
10.9%
B1244
 
10.7%
M1106
 
9.5%
O847
 
7.3%
F789
 
6.8%
H555
 
4.8%
Y540
 
4.6%
Z528
 
4.5%
W349
 
3.0%
K339
 
2.9%
Other values (14)4096
35.1%
Decimal Number
ValueCountFrequency (%)
21731
14.4%
11435
11.9%
81345
11.2%
51280
10.6%
61269
10.6%
31196
10.0%
41171
9.7%
9962
8.0%
7841
7.0%
0789
6.6%
Other Punctuation
ValueCountFrequency (%)
/4235
55.6%
.1694
 
22.2%
?847
 
11.1%
:847
 
11.1%
Dash Punctuation
ValueCountFrequency (%)
-4958
100.0%
Math Symbol
ValueCountFrequency (%)
=847
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin56866
69.1%
Common25447
30.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
p4343
 
7.6%
t4109
 
7.2%
i3718
 
6.5%
a3138
 
5.5%
w2709
 
4.8%
e2558
 
4.5%
m2541
 
4.5%
c2114
 
3.7%
o2096
 
3.7%
l2056
 
3.6%
Other values (40)27484
48.3%
Common
ValueCountFrequency (%)
-4958
19.5%
/4235
16.6%
21731
 
6.8%
.1694
 
6.7%
11435
 
5.6%
81345
 
5.3%
51280
 
5.0%
61269
 
5.0%
31196
 
4.7%
41171
 
4.6%
Other values (6)5133
20.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII82313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-4958
 
6.0%
p4343
 
5.3%
/4235
 
5.1%
t4109
 
5.0%
i3718
 
4.5%
a3138
 
3.8%
w2709
 
3.3%
e2558
 
3.1%
m2541
 
3.1%
c2114
 
2.6%
Other values (56)47890
58.2%

product_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct847
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
MOBG73E7GKQK4KZP
 
1
MOBFUYUNHENTTY9M
 
1
MOBG6VF5WKMAADTZ
 
1
MOBGB74URFDJ3EGH
 
1
MOBF8FCZHK2HWJVT
 
1
Other values (842)
842 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters13552
Distinct characters32
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique847 ?
Unique (%)100.0%

Sample

1st rowMOBG73E7GKQK4KZP
2nd rowMOBG73E7UBFXXMCH
3rd rowMOBGBTHFSKHF8RAU
4th rowMOBGCFUHMDFSCM9W
5th rowMOBG9CJ6G5GCFAH4

Common Values

ValueCountFrequency (%)
MOBG73E7GKQK4KZP1
 
0.1%
MOBFUYUNHENTTY9M1
 
0.1%
MOBG6VF5WKMAADTZ1
 
0.1%
MOBGB74URFDJ3EGH1
 
0.1%
MOBF8FCZHK2HWJVT1
 
0.1%
MOBFWSF8KRVUGH9W1
 
0.1%
MOBFZ2U4GURCS37U1
 
0.1%
MOBGYV9VUGTPFWRY1
 
0.1%
MOBFRZZHZGSRRWUZ1
 
0.1%
MOBGC9K3EMZYFNDT1
 
0.1%
Other values (837)837
98.8%

Length

2022-08-13T15:02:07.388428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mobg73e7gkqk4kzp1
 
0.1%
mobg23kttgynzfrm1
 
0.1%
mobgbmzh9x7pqzjz1
 
0.1%
mobgbthfskhf8rau1
 
0.1%
mobgcfuhmdfscm9w1
 
0.1%
mobg9cj6g5gcfah41
 
0.1%
mobgf489sqzcfhya1
 
0.1%
mobgf4894mewzjgv1
 
0.1%
mobg73e7zbhhq3rb1
 
0.1%
mobg9vgvkum4xnrr1
 
0.1%
Other values (837)837
98.8%

Most occurring characters

ValueCountFrequency (%)
G1271
 
9.4%
B1244
 
9.2%
M1106
 
8.2%
O847
 
6.2%
F789
 
5.8%
H555
 
4.1%
Y540
 
4.0%
Z528
 
3.9%
W349
 
2.6%
K339
 
2.5%
Other values (22)5984
44.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter11664
86.1%
Decimal Number1888
 
13.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G1271
 
10.9%
B1244
 
10.7%
M1106
 
9.5%
O847
 
7.3%
F789
 
6.8%
H555
 
4.8%
Y540
 
4.6%
Z528
 
4.5%
W349
 
3.0%
K339
 
2.9%
Other values (14)4096
35.1%
Decimal Number
ValueCountFrequency (%)
5277
14.7%
6272
14.4%
8240
12.7%
9239
12.7%
4231
12.2%
2217
11.5%
3215
11.4%
7197
10.4%

Most occurring scripts

ValueCountFrequency (%)
Latin11664
86.1%
Common1888
 
13.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
G1271
 
10.9%
B1244
 
10.7%
M1106
 
9.5%
O847
 
7.3%
F789
 
6.8%
H555
 
4.8%
Y540
 
4.6%
Z528
 
4.5%
W349
 
3.0%
K339
 
2.9%
Other values (14)4096
35.1%
Common
ValueCountFrequency (%)
5277
14.7%
6272
14.4%
8240
12.7%
9239
12.7%
4231
12.2%
2217
11.5%
3215
11.4%
7197
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII13552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G1271
 
9.4%
B1244
 
9.2%
M1106
 
8.2%
O847
 
6.2%
F789
 
5.8%
H555
 
4.1%
Y540
 
4.0%
Z528
 
3.9%
W349
 
2.6%
K339
 
2.5%
Other values (22)5984
44.2%

listing_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct847
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
LSTMOBG73E7GKQK4KZPR5ICMK
 
1
LSTMOBFUYUNHENTTY9MSTBB71
 
1
LSTMOBG6VF5WKMAADTZP6DVL2
 
1
LSTMOBGB74URFDJ3EGHWPSDGQ
 
1
LSTMOBF8FCZHK2HWJVTLHKVJA
 
1
Other values (842)
842 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters21175
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique847 ?
Unique (%)100.0%

Sample

1st rowLSTMOBG73E7GKQK4KZPR5ICMK
2nd rowLSTMOBG73E7UBFXXMCHSM0ZN5
3rd rowLSTMOBGBTHFSKHF8RAUQONXWY
4th rowLSTMOBGCFUHMDFSCM9WSL0U8O
5th rowLSTMOBG9CJ6G5GCFAH4CCXVXL

Common Values

ValueCountFrequency (%)
LSTMOBG73E7GKQK4KZPR5ICMK1
 
0.1%
LSTMOBFUYUNHENTTY9MSTBB711
 
0.1%
LSTMOBG6VF5WKMAADTZP6DVL21
 
0.1%
LSTMOBGB74URFDJ3EGHWPSDGQ1
 
0.1%
LSTMOBF8FCZHK2HWJVTLHKVJA1
 
0.1%
LSTMOBFWSF8KRVUGH9WJ4EGIM1
 
0.1%
LSTMOBFZ2U4GURCS37UYYS09Z1
 
0.1%
LSTMOBGYV9VUGTPFWRYJFN6FU1
 
0.1%
LSTMOBFRZZHZGSRRWUZY4LQJU1
 
0.1%
LSTMOBGC9K3EMZYFNDTWRLKFS1
 
0.1%
Other values (837)837
98.8%

Length

2022-08-13T15:02:07.528976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lstmobg73e7gkqk4kzpr5icmk1
 
0.1%
lstmobg23kttgynzfrmyw1a2d1
 
0.1%
lstmobgbmzh9x7pqzjznqtn2q1
 
0.1%
lstmobgbthfskhf8rauqonxwy1
 
0.1%
lstmobgcfuhmdfscm9wsl0u8o1
 
0.1%
lstmobg9cj6g5gcfah4ccxvxl1
 
0.1%
lstmobgf489sqzcfhyagb7sds1
 
0.1%
lstmobgf4894mewzjgvw425n51
 
0.1%
lstmobg73e7zbhhq3rbdyspvo1
 
0.1%
lstmobg9vgvkum4xnrrjw5ynz1
 
0.1%
Other values (837)837
98.8%

Most occurring characters

ValueCountFrequency (%)
G1427
 
6.7%
B1404
 
6.6%
T1311
 
6.2%
S1283
 
6.1%
M1275
 
6.0%
O1019
 
4.8%
L1004
 
4.7%
F946
 
4.5%
H718
 
3.4%
Y702
 
3.3%
Other values (26)10086
47.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter18459
87.2%
Decimal Number2716
 
12.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G1427
 
7.7%
B1404
 
7.6%
T1311
 
7.1%
S1283
 
7.0%
M1275
 
6.9%
O1019
 
5.5%
L1004
 
5.4%
F946
 
5.1%
H718
 
3.9%
Y702
 
3.8%
Other values (16)7370
39.9%
Decimal Number
ValueCountFrequency (%)
5360
13.3%
6343
12.6%
9331
12.2%
8325
12.0%
4314
11.6%
2306
11.3%
3279
10.3%
7277
10.2%
191
 
3.4%
090
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin18459
87.2%
Common2716
 
12.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
G1427
 
7.7%
B1404
 
7.6%
T1311
 
7.1%
S1283
 
7.0%
M1275
 
6.9%
O1019
 
5.5%
L1004
 
5.4%
F946
 
5.1%
H718
 
3.9%
Y702
 
3.8%
Other values (16)7370
39.9%
Common
ValueCountFrequency (%)
5360
13.3%
6343
12.6%
9331
12.2%
8325
12.0%
4314
11.6%
2306
11.3%
3279
10.3%
7277
10.2%
191
 
3.4%
090
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII21175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G1427
 
6.7%
B1404
 
6.6%
T1311
 
6.2%
S1283
 
6.1%
M1275
 
6.0%
O1019
 
4.8%
L1004
 
4.7%
F946
 
4.5%
H718
 
3.4%
Y702
 
3.3%
Other values (26)10086
47.6%

highlights
Categorical

HIGH CARDINALITY
UNIFORM

Distinct489
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
128 GB ROM 13.72 cm (5.4 inch) Super Retina XDR Display 12MP + 12MP | 12MP Front Camera A15 Bionic Chip Processor
 
6
128 GB ROM 15.49 cm (6.1 inch) Super Retina XDR Display 12MP + 12MP | 12MP Front Camera A15 Bionic Chip Processor
 
5
64 GB ROM 15.49 cm (6.1 inch) Liquid Retina HD Display 12MP + 12MP | 12MP Front Camera A13 Bionic Chip Processor
 
5
3 GB RAM | 32 GB ROM | Expandable Upto 512 GB 16.59 cm (6.53 inch) HD+ Display 13MP Rear Camera | 5MP Front Camera 5000 mAh Battery MediaTek Helio G25 Processor
 
5
128 GB ROM 13.72 cm (5.4 inch) Super Retina XDR Display 12MP + 12MP | 12MP Front Camera A14 Bionic Chip with Next Generation Neural Engine Processor Ceramic Shield Industry-leading IP68 Water Resistance All Screen OLED Display 12MP TrueDepth Front Camera with Night Mode, 4K Dolby Vision HDR Recording
 
4
Other values (484)
822 

Length

Max length420
Median length248
Mean length171.0177096
Min length81

Characters and Unicode

Total characters144852
Distinct characters75
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique236 ?
Unique (%)27.9%

Sample

1st row4 GB RAM | 64 GB ROM | Expandable Upto 512 GB 16.59 cm (6.53 inch) HD+ Display 13MP + 2MP + 2MP | 5MP Front Camera 5000 mAh Lithium-ion Polymer Battery MediaTek Helio G35 Processor
2nd row4 GB RAM | 64 GB ROM | Expandable Upto 512 GB 16.59 cm (6.53 inch) HD+ Display 13MP + 2MP + 2MP | 5MP Front Camera 5000 mAh Lithium-ion Polymer Battery MediaTek Helio G35 Processor
3rd row4 GB RAM | 64 GB ROM | Expandable Upto 1 TB 16.76 cm (6.6 inch) Full HD+ Display 50MP + 2MP + 0.3MP | 8MP Front Camera 5000 mAh Lithium Polymer Battery Unisoc Tiger T616 Processor
4th row6 GB RAM | 128 GB ROM | Expandable Upto 1 TB 16.74 cm (6.59 inch) Full HD+ Display 50MP + 2MP + 2MP | 16MP Front Camera 5000 mAh Lithium Ion Battery Qualcomm Snapdragon 680 Processor 33W SUPERVOOC Charger | Dual Speaker | Super Adaptive Refresh Rate AI Photo Suite | OPPO Glow Design with Dirt and Scratch Resistant
5th row6 GB RAM | 128 GB ROM 17.22 cm (6.78 inch) Full HD+ Display 108MP + 8MP + 2MP | 32MP Front Camera 6000 mAh Battery Qualcomm Snapdragon 732G Processor 120Hz Refresh Rate Stock Android Experience

Common Values

ValueCountFrequency (%)
128 GB ROM 13.72 cm (5.4 inch) Super Retina XDR Display 12MP + 12MP | 12MP Front Camera A15 Bionic Chip Processor6
 
0.7%
128 GB ROM 15.49 cm (6.1 inch) Super Retina XDR Display 12MP + 12MP | 12MP Front Camera A15 Bionic Chip Processor5
 
0.6%
64 GB ROM 15.49 cm (6.1 inch) Liquid Retina HD Display 12MP + 12MP | 12MP Front Camera A13 Bionic Chip Processor5
 
0.6%
3 GB RAM | 32 GB ROM | Expandable Upto 512 GB 16.59 cm (6.53 inch) HD+ Display 13MP Rear Camera | 5MP Front Camera 5000 mAh Battery MediaTek Helio G25 Processor5
 
0.6%
128 GB ROM 13.72 cm (5.4 inch) Super Retina XDR Display 12MP + 12MP | 12MP Front Camera A14 Bionic Chip with Next Generation Neural Engine Processor Ceramic Shield Industry-leading IP68 Water Resistance All Screen OLED Display 12MP TrueDepth Front Camera with Night Mode, 4K Dolby Vision HDR Recording4
 
0.5%
256 GB ROM 13.72 cm (5.4 inch) Super Retina XDR Display 12MP + 12MP | 12MP Front Camera A15 Bionic Chip Processor4
 
0.5%
4 GB RAM | 128 GB ROM | Expandable Upto 256 GB 17.22 cm (6.78 inch) Full HD+ Display 50 MP + 2 MP + AI Lens | 8MP Front Camera 5000 mAh Li-ion Polymer Battery Mediatek Helio G88 Processor4
 
0.5%
64 GB ROM 15.49 cm (6.1 inch) Super Retina XDR Display 12MP + 12MP | 12MP Front Camera A14 Bionic Chip with Next Generation Neural Engine Processor Ceramic Shield Industry-leading IP68 Water Resistance All Screen OLED Display 12MP TrueDepth Front Camera with Night Mode, 4K Dolby Vision HDR Recording4
 
0.5%
6 GB RAM | 128 GB ROM | Expandable Upto 1 TB 16.94 cm (6.67 inch) Full HD+ AMOLED Display 108MP Rear Camera | 16MP Front Camera 5160 mAh Li-Polymer Battery Mediatek Dimensity 920 Processor4
 
0.5%
128 GB ROM 17.02 cm (6.7 inch) Super Retina XDR Display 12MP + 12MP + 12MP | 12MP Front Camera A15 Bionic Chip Processor4
 
0.5%
Other values (479)802
94.7%

Length

2022-08-13T15:02:07.732051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3324
 
11.5%
gb1958
 
6.8%
camera1036
 
3.6%
display921
 
3.2%
rom847
 
2.9%
cm847
 
2.9%
inch847
 
2.9%
processor779
 
2.7%
front777
 
2.7%
battery753
 
2.6%
Other values (585)16734
58.1%

Most occurring characters

ValueCountFrequency (%)
27976
 
19.3%
a6793
 
4.7%
e6032
 
4.2%
r5746
 
4.0%
M4979
 
3.4%
o4680
 
3.2%
i4504
 
3.1%
t4092
 
2.8%
P3969
 
2.7%
n3747
 
2.6%
Other values (65)72334
49.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60551
41.8%
Uppercase Letter28816
19.9%
Space Separator27976
19.3%
Decimal Number19508
 
13.5%
Math Symbol3946
 
2.7%
Other Punctuation1860
 
1.3%
Open Punctuation926
 
0.6%
Close Punctuation920
 
0.6%
Dash Punctuation343
 
0.2%
Other Symbol6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a6793
11.2%
e6032
10.0%
r5746
 
9.5%
o4680
 
7.7%
i4504
 
7.4%
t4092
 
6.8%
n3747
 
6.2%
m3676
 
6.1%
l3234
 
5.3%
c3122
 
5.2%
Other values (15)14925
24.6%
Uppercase Letter
ValueCountFrequency (%)
M4979
17.3%
P3969
13.8%
B3008
10.4%
G2278
7.9%
R2147
7.5%
D2063
7.2%
A1857
 
6.4%
C1418
 
4.9%
F1241
 
4.3%
O1070
 
3.7%
Other values (15)4786
16.6%
Decimal Number
ValueCountFrequency (%)
63200
16.4%
13018
15.5%
22829
14.5%
02679
13.7%
52442
12.5%
81623
8.3%
41462
7.5%
31253
 
6.4%
7589
 
3.0%
9413
 
2.1%
Other Punctuation
ValueCountFrequency (%)
.1765
94.9%
,83
 
4.5%
:7
 
0.4%
/2
 
0.1%
%1
 
0.1%
?1
 
0.1%
'1
 
0.1%
Math Symbol
ValueCountFrequency (%)
|2098
53.2%
+1848
46.8%
Other Symbol
ValueCountFrequency (%)
®3
50.0%
3
50.0%
Space Separator
ValueCountFrequency (%)
27976
100.0%
Open Punctuation
ValueCountFrequency (%)
(926
100.0%
Close Punctuation
ValueCountFrequency (%)
)920
100.0%
Dash Punctuation
ValueCountFrequency (%)
-343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin89367
61.7%
Common55485
38.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a6793
 
7.6%
e6032
 
6.7%
r5746
 
6.4%
M4979
 
5.6%
o4680
 
5.2%
i4504
 
5.0%
t4092
 
4.6%
P3969
 
4.4%
n3747
 
4.2%
m3676
 
4.1%
Other values (40)41149
46.0%
Common
ValueCountFrequency (%)
27976
50.4%
63200
 
5.8%
13018
 
5.4%
22829
 
5.1%
02679
 
4.8%
52442
 
4.4%
|2098
 
3.8%
+1848
 
3.3%
.1765
 
3.2%
81623
 
2.9%
Other values (15)6007
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII144846
> 99.9%
None3
 
< 0.1%
Letterlike Symbols3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27976
 
19.3%
a6793
 
4.7%
e6032
 
4.2%
r5746
 
4.0%
M4979
 
3.4%
o4680
 
3.2%
i4504
 
3.1%
t4092
 
2.8%
P3969
 
2.7%
n3747
 
2.6%
Other values (63)72328
49.9%
None
ValueCountFrequency (%)
®3
100.0%
Letterlike Symbols
ValueCountFrequency (%)
3
100.0%

availability
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
IN_STOCK
721 
COMING_SOON
93 
OUT_OF_STOCK
 
32
PRE_ORDER
 
1

Length

Max length12
Median length8
Mean length8.481700118
Min length8

Characters and Unicode

Total characters7184
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowIN_STOCK
2nd rowIN_STOCK
3rd rowIN_STOCK
4th rowCOMING_SOON
5th rowIN_STOCK

Common Values

ValueCountFrequency (%)
IN_STOCK721
85.1%
COMING_SOON93
 
11.0%
OUT_OF_STOCK32
 
3.8%
PRE_ORDER1
 
0.1%

Length

2022-08-13T15:02:07.935136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-13T15:02:08.106967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
in_stock721
85.1%
coming_soon93
 
11.0%
out_of_stock32
 
3.8%
pre_order1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
O1097
15.3%
N907
12.6%
_879
12.2%
S846
11.8%
C846
11.8%
I814
11.3%
T785
10.9%
K753
10.5%
M93
 
1.3%
G93
 
1.3%
Other values (6)71
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6305
87.8%
Connector Punctuation879
 
12.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O1097
17.4%
N907
14.4%
S846
13.4%
C846
13.4%
I814
12.9%
T785
12.5%
K753
11.9%
M93
 
1.5%
G93
 
1.5%
U32
 
0.5%
Other values (5)39
 
0.6%
Connector Punctuation
ValueCountFrequency (%)
_879
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6305
87.8%
Common879
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
O1097
17.4%
N907
14.4%
S846
13.4%
C846
13.4%
I814
12.9%
T785
12.5%
K753
11.9%
M93
 
1.5%
G93
 
1.5%
U32
 
0.5%
Other values (5)39
 
0.6%
Common
ValueCountFrequency (%)
_879
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O1097
15.3%
N907
12.6%
_879
12.2%
S846
11.8%
C846
11.8%
I814
11.3%
T785
10.9%
K753
10.5%
M93
 
1.3%
G93
 
1.3%
Other values (6)71
 
1.0%

selling_price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct333
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24810.7686
Minimum3780
Maximum179900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-08-13T15:02:08.294420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3780
5-th percentile6999
Q110990
median15499
Q324999
95-th percentile84900
Maximum179900
Range176120
Interquartile range (IQR)14009

Descriptive statistics

Standard deviation27771.02882
Coefficient of variation (CV)1.119313524
Kurtosis10.19080004
Mean24810.7686
Median Absolute Deviation (MAD)5500
Skewness3.077610925
Sum21014721
Variance771230041.7
MonotonicityNot monotonic
2022-08-13T15:02:08.481879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1499921
 
2.5%
1699920
 
2.4%
1199920
 
2.4%
899917
 
2.0%
749916
 
1.9%
799916
 
1.9%
1099916
 
1.9%
1799915
 
1.8%
1999915
 
1.8%
1549914
 
1.7%
Other values (323)677
79.9%
ValueCountFrequency (%)
37801
0.1%
42491
0.1%
44901
0.1%
48481
0.1%
49901
0.1%
50991
0.1%
51491
0.1%
51801
0.1%
58871
0.1%
58902
0.2%
ValueCountFrequency (%)
1799001
 
0.1%
1699004
0.5%
1599002
 
0.2%
1499991
 
0.1%
1499002
 
0.2%
1403001
 
0.1%
1399003
 
0.4%
1299901
 
0.1%
1299008
0.9%
1199004
0.5%

original_price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct155
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27748.35301
Minimum4999
Maximum179900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-08-13T15:02:08.700614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4999
5-th percentile7999
Q112999
median17999
Q327999
95-th percentile89900
Maximum179900
Range174901
Interquartile range (IQR)15000

Descriptive statistics

Standard deviation28466.64267
Coefficient of variation (CV)1.025885848
Kurtosis8.386181474
Mean27748.35301
Median Absolute Deviation (MAD)6500
Skewness2.803055262
Sum23502855
Variance810349744.8
MonotonicityNot monotonic
2022-08-13T15:02:08.888031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1699948
 
5.7%
1899930
 
3.5%
1299929
 
3.4%
1799928
 
3.3%
1499927
 
3.2%
999926
 
3.1%
1999923
 
2.7%
1399922
 
2.6%
899922
 
2.6%
799920
 
2.4%
Other values (145)572
67.5%
ValueCountFrequency (%)
49993
0.4%
50901
 
0.1%
50991
 
0.1%
51491
 
0.1%
52991
 
0.1%
59993
0.4%
64001
 
0.1%
64991
 
0.1%
67993
0.4%
69901
 
0.1%
ValueCountFrequency (%)
1799001
 
0.1%
1699004
0.5%
1599002
 
0.2%
1499991
 
0.1%
1499002
 
0.2%
1403001
 
0.1%
1399004
0.5%
1299008
0.9%
1199006
0.7%
1171002
 
0.2%

currency
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
INR
847 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2541
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINR
2nd rowINR
3rd rowINR
4th rowINR
5th rowINR

Common Values

ValueCountFrequency (%)
INR847
100.0%

Length

2022-08-13T15:02:09.075531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-13T15:02:09.216119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
inr847
100.0%

Most occurring characters

ValueCountFrequency (%)
I847
33.3%
N847
33.3%
R847
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2541
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I847
33.3%
N847
33.3%
R847
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2541
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I847
33.3%
N847
33.3%
R847
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2541
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I847
33.3%
N847
33.3%
R847
33.3%

avg_rating
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.178748524
Minimum0
Maximum5
Zeros22
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-08-13T15:02:09.544130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.73
Q14.2
median4.3
Q34.4
95-th percentile4.6
Maximum5
Range5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.7236608023
Coefficient of variation (CV)0.1731764422
Kurtosis26.30453662
Mean4.178748524
Median Absolute Deviation (MAD)0.1
Skewness-5.052953371
Sum3539.4
Variance0.5236849568
MonotonicityNot monotonic
2022-08-13T15:02:09.684762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4.3266
31.4%
4.4139
16.4%
4.2138
16.3%
4.665
 
7.7%
4.561
 
7.2%
4.141
 
4.8%
434
 
4.0%
4.726
 
3.1%
022
 
2.6%
3.920
 
2.4%
Other values (9)35
 
4.1%
ValueCountFrequency (%)
022
2.6%
1.91
 
0.1%
2.92
 
0.2%
32
 
0.2%
3.45
 
0.6%
3.51
 
0.1%
3.62
 
0.2%
3.78
 
0.9%
3.812
1.4%
3.920
2.4%
ValueCountFrequency (%)
52
 
0.2%
4.726
 
3.1%
4.665
 
7.7%
4.561
 
7.2%
4.4139
16.4%
4.3266
31.4%
4.2138
16.3%
4.141
 
4.8%
434
 
4.0%
3.920
 
2.4%

ratings_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct387
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37161.18418
Minimum0
Maximum912314
Zeros22
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-08-13T15:02:09.856561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.5
Q1874.5
median4237
Q331605
95-th percentile166805
Maximum912314
Range912314
Interquartile range (IQR)30730.5

Descriptive statistics

Standard deviation85815.34415
Coefficient of variation (CV)2.309273669
Kurtosis34.80297527
Mean37161.18418
Median Absolute Deviation (MAD)4197
Skewness4.988764245
Sum31475523
Variance7364273292
MonotonicityNot monotonic
2022-08-13T15:02:10.059633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022
 
2.6%
56913
 
1.5%
43713
 
1.5%
309111
 
1.3%
14761711
 
1.3%
1471610
 
1.2%
1668059
 
1.1%
8789
 
1.1%
784959
 
1.1%
953159
 
1.1%
Other values (377)731
86.3%
ValueCountFrequency (%)
022
2.6%
34
 
0.5%
46
 
0.7%
53
 
0.4%
92
 
0.2%
122
 
0.2%
131
 
0.1%
163
 
0.4%
214
 
0.5%
232
 
0.2%
ValueCountFrequency (%)
9123142
0.2%
5583282
0.2%
5555843
0.4%
4551031
 
0.1%
4023351
 
0.1%
3898971
 
0.1%
3872891
 
0.1%
3654331
 
0.1%
3585241
 
0.1%
3576451
 
0.1%

reviews_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct343
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2989.429752
Minimum0
Maximum71867
Zeros32
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-08-13T15:02:10.247092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q170
median441
Q32910.5
95-th percentile11610
Maximum71867
Range71867
Interquartile range (IQR)2840.5

Descriptive statistics

Standard deviation6679.098769
Coefficient of variation (CV)2.234238408
Kurtosis36.91005337
Mean2989.429752
Median Absolute Deviation (MAD)432
Skewness5.112904974
Sum2532047
Variance44610360.37
MonotonicityNot monotonic
2022-08-13T15:02:10.434589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032
 
3.8%
121
 
2.5%
4013
 
1.5%
5513
 
1.5%
111112
 
1.4%
33211
 
1.3%
1161011
 
1.3%
9410
 
1.2%
59219
 
1.1%
114559
 
1.1%
Other values (333)706
83.4%
ValueCountFrequency (%)
032
3.8%
121
2.5%
24
 
0.5%
43
 
0.4%
54
 
0.5%
74
 
0.5%
84
 
0.5%
95
 
0.6%
105
 
0.6%
112
 
0.2%
ValueCountFrequency (%)
718672
0.2%
547771
 
0.1%
406712
0.2%
398021
 
0.1%
354511
 
0.1%
331663
0.4%
329322
0.2%
295901
 
0.1%
295721
 
0.1%
282551
 
0.1%

one_stars_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct333
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1866.495868
Minimum0
Maximum36443
Zeros39
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-08-13T15:02:10.637628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q163
median331
Q31848
95-th percentile8436.1
Maximum36443
Range36443
Interquartile range (IQR)1785

Descriptive statistics

Standard deviation4000.221087
Coefficient of variation (CV)2.143171681
Kurtosis26.42828956
Mean1866.495868
Median Absolute Deviation (MAD)320
Skewness4.50097452
Sum1580922
Variance16001768.74
MonotonicityNot monotonic
2022-08-13T15:02:10.825121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
039
 
4.6%
1713
 
1.5%
3013
 
1.5%
5012
 
1.4%
13411
 
1.3%
609211
 
1.3%
6610
 
1.2%
108510
 
1.2%
49
 
1.1%
30199
 
1.1%
Other values (323)710
83.8%
ValueCountFrequency (%)
039
4.6%
15
 
0.6%
28
 
0.9%
35
 
0.6%
49
 
1.1%
51
 
0.1%
62
 
0.2%
74
 
0.5%
94
 
0.5%
107
 
0.8%
ValueCountFrequency (%)
364432
0.2%
298723
0.4%
257132
0.2%
220621
 
0.1%
211851
 
0.1%
182721
 
0.1%
182431
 
0.1%
171372
0.2%
170831
 
0.1%
162621
 
0.1%

two_stars_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct289
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean894.1404959
Minimum0
Maximum20581
Zeros44
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-08-13T15:02:11.028198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123.5
median141
Q3821.5
95-th percentile4797
Maximum20581
Range20581
Interquartile range (IQR)798

Descriptive statistics

Standard deviation2150.423879
Coefficient of variation (CV)2.405017879
Kurtosis33.26152216
Mean894.1404959
Median Absolute Deviation (MAD)137
Skewness5.115467578
Sum757337
Variance4624322.861
MonotonicityNot monotonic
2022-08-13T15:02:11.215654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
044
 
5.2%
630
 
3.5%
422
 
2.6%
721
 
2.5%
320
 
2.4%
3620
 
2.4%
1514
 
1.7%
3311
 
1.3%
211
 
1.3%
221211
 
1.3%
Other values (279)643
75.9%
ValueCountFrequency (%)
044
5.2%
18
 
0.9%
211
 
1.3%
320
2.4%
422
2.6%
54
 
0.5%
630
3.5%
721
2.5%
81
 
0.1%
98
 
0.9%
ValueCountFrequency (%)
205812
0.2%
172283
0.4%
135902
0.2%
127281
 
0.1%
109981
 
0.1%
102561
 
0.1%
101121
 
0.1%
100601
 
0.1%
89591
 
0.1%
87631
 
0.1%

three_stars_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct332
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2582.670602
Minimum0
Maximum63157
Zeros34
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-08-13T15:02:11.418731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q161
median357
Q32435
95-th percentile12107
Maximum63157
Range63157
Interquartile range (IQR)2374

Descriptive statistics

Standard deviation6398.502806
Coefficient of variation (CV)2.47747537
Kurtosis34.96064601
Mean2582.670602
Median Absolute Deviation (MAD)351
Skewness5.253939483
Sum2187522
Variance40940838.16
MonotonicityNot monotonic
2022-08-13T15:02:11.606151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
4.0%
119
 
2.2%
817
 
2.0%
6113
 
1.5%
1613
 
1.5%
627011
 
1.3%
1510
 
1.2%
108710
 
1.2%
25609
 
1.1%
37509
 
1.1%
Other values (322)702
82.9%
ValueCountFrequency (%)
034
4.0%
119
2.2%
24
 
0.5%
36
 
0.7%
43
 
0.4%
53
 
0.4%
67
 
0.8%
71
 
0.1%
817
2.0%
93
 
0.4%
ValueCountFrequency (%)
631572
0.2%
498753
0.4%
412752
0.2%
357891
 
0.1%
356171
 
0.1%
321851
 
0.1%
316201
 
0.1%
300101
 
0.1%
273001
 
0.1%
249691
 
0.1%

four_stars_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct363
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7494.469894
Minimum0
Maximum181356
Zeros33
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-08-13T15:02:11.809271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1142.5
median922
Q36750.5
95-th percentile31349
Maximum181356
Range181356
Interquartile range (IQR)6608

Descriptive statistics

Standard deviation17820.22977
Coefficient of variation (CV)2.377783888
Kurtosis33.49749995
Mean7494.469894
Median Absolute Deviation (MAD)911
Skewness5.052240433
Sum6347816
Variance317560588.9
MonotonicityNot monotonic
2022-08-13T15:02:11.996684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
3.9%
4416
 
1.9%
5713
 
1.5%
2481711
 
1.3%
35211
 
1.3%
111
 
1.3%
5110
 
1.2%
301210
 
1.2%
109949
 
1.1%
147279
 
1.1%
Other values (353)714
84.3%
ValueCountFrequency (%)
033
3.9%
111
 
1.3%
21
 
0.1%
31
 
0.1%
43
 
0.4%
55
 
0.6%
64
 
0.5%
76
 
0.7%
84
 
0.5%
91
 
0.1%
ValueCountFrequency (%)
1813562
0.2%
1260093
0.4%
1128012
0.2%
931601
 
0.1%
900711
 
0.1%
898731
 
0.1%
871881
 
0.1%
839021
 
0.1%
813322
0.2%
726461
 
0.1%

five_stars_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct390
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24323.40732
Minimum0
Maximum610777
Zeros22
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2022-08-13T15:02:12.199762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q1542.5
median2658
Q320303.5
95-th percentile120501.3
Maximum610777
Range610777
Interquartile range (IQR)19761

Descriptive statistics

Standard deviation56116.52844
Coefficient of variation (CV)2.307099811
Kurtosis35.68986269
Mean24323.40732
Median Absolute Deviation (MAD)2616
Skewness4.961525637
Sum20601926
Variance3149064764
MonotonicityNot monotonic
2022-08-13T15:02:12.387214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022
 
2.6%
34113
 
1.5%
48313
 
1.5%
10822611
 
1.3%
250811
 
1.3%
905710
 
1.2%
1331249
 
1.1%
7569
 
1.1%
706669
 
1.1%
609819
 
1.1%
Other values (380)731
86.3%
ValueCountFrequency (%)
022
2.6%
13
 
0.4%
25
 
0.6%
36
 
0.7%
71
 
0.1%
85
 
0.6%
93
 
0.4%
111
 
0.1%
135
 
0.6%
141
 
0.1%
ValueCountFrequency (%)
6107772
0.2%
3649492
0.2%
3326003
0.4%
2973361
 
0.1%
2497121
 
0.1%
2491711
 
0.1%
2410111
 
0.1%
2380881
 
0.1%
2349681
 
0.1%
2200801
 
0.1%

Interactions

2022-08-13T15:02:01.753804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:44.812123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:46.949208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:48.960618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:50.688461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:52.411502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:54.226759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:56.241761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:58.032791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:59.854541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:02.143994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:45.296635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:47.139641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:49.136488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:50.874329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:52.583321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:54.407054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:56.425439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:58.215203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:00.050547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:02.341866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:45.484455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:47.323693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:49.319003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:51.054832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:52.783044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:54.590429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:56.609014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:58.408770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:00.262615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:02.511922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:45.658252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:47.498609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:49.481381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:51.219386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:52.955306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:54.760059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:56.786360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:58.573617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:00.446849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:02.686292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:45.839587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:47.671989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:49.641845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:51.377954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:53.117228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:55.141524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:56.952775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:58.743831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:00.618463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:02.871385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:46.027738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:47.851839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:49.817213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:51.553468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:53.317125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:55.316740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:57.131719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:58.940090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:00.811580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:03.052246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:46.220437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:48.049374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:49.998363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:51.731376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:53.506917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:55.512433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:57.322728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:59.123689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:01.005816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:03.221886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:46.389243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:48.386427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:50.161108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:51.893108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:53.673087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:55.686248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:57.495272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:59.295293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:01.177425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:03.399760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:46.572215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:48.573876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:50.334964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:52.060226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:53.853627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:55.871262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:57.673502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:59.481836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:01.378244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:03.595571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:46.763266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:48.778994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:50.512741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:52.244258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:54.043913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:56.061502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:57.854565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:01:59.663125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-13T15:02:01.572941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-13T15:02:12.605918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-13T15:02:12.887143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-13T15:02:13.137043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-13T15:02:13.387027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-13T15:02:13.574476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-13T15:02:03.930180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-13T15:02:04.469825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-13T15:02:05.177392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

titlerambrandurlproduct_idlisting_idhighlightsavailabilityselling_priceoriginal_pricecurrencyavg_ratingratings_countreviews_countone_stars_counttwo_stars_countthree_stars_countfour_stars_countfive_stars_count
0POCO C31 (Royal Blue, 64 GB)4 GB RAMPOCOhttps://www.flipkart.com/poco-c31-royal-blue-64-gb/p/itm19effae969b86?pid=MOBG73E7GKQK4KZPMOBG73E7GKQK4KZPLSTMOBG73E7GKQK4KZPR5ICMK4 GB RAM | 64 GB ROM | Expandable Upto 512 GB 16.59 cm (6.53 inch) HD+ Display 13MP + 2MP + 2MP | 5MP Front Camera 5000 mAh Lithium-ion Polymer Battery MediaTek Helio G35 ProcessorIN_STOCK8999.011999.0INR4.434176195716758382678670722278
1POCO C31 (Shadow Gray, 64 GB)4 GB RAMPOCOhttps://www.flipkart.com/poco-c31-shadow-gray-64-gb/p/itm162375acb8370?pid=MOBG73E7UBFXXMCHMOBG73E7UBFXXMCHLSTMOBG73E7UBFXXMCHSM0ZN54 GB RAM | 64 GB ROM | Expandable Upto 512 GB 16.59 cm (6.53 inch) HD+ Display 13MP + 2MP + 2MP | 5MP Front Camera 5000 mAh Lithium-ion Polymer Battery MediaTek Helio G35 ProcessorIN_STOCK8999.011999.0INR4.434176195716758382678670722278
2realme C35 (Glowing Green, 64 GB)4 GB RAMrealmehttps://www.flipkart.com/realme-c35-glowing-green-64-gb/p/itmafb045222b2cf?pid=MOBGBTHFSKHF8RAUMOBGBTHFSKHF8RAULSTMOBGBTHFSKHF8RAUQONXWY4 GB RAM | 64 GB ROM | Expandable Upto 1 TB 16.76 cm (6.6 inch) Full HD+ Display 50MP + 2MP + 0.3MP | 8MP Front Camera 5000 mAh Lithium Polymer Battery Unisoc Tiger T616 ProcessorIN_STOCK11999.013999.0INR4.4856359059020048713035983
3OPPO K10 (Black Carbon, 128 GB)6 GB RAMOPPOhttps://www.flipkart.com/oppo-k10-black-carbon-128-gb/p/itm6205e7e72fe0c?pid=MOBGCFUHMDFSCM9WMOBGCFUHMDFSCM9WLSTMOBGCFUHMDFSCM9WSL0U8O6 GB RAM | 128 GB ROM | Expandable Upto 1 TB 16.74 cm (6.59 inch) Full HD+ Display 50MP + 2MP + 2MP | 16MP Front Camera 5000 mAh Lithium Ion Battery Qualcomm Snapdragon 680 Processor 33W SUPERVOOC Charger | Dual Speaker | Super Adaptive Refresh Rate AI Photo Suite | OPPO Glow Design with Dirt and Scratch ResistantCOMING_SOON14990.018999.0INR0.00000000
4MOTOROLA G60 (Soft Silver, 128 GB)6 GB RAMMOTOROLAhttps://www.flipkart.com/motorola-g60-soft-silver-128-gb/p/itm7d158ff189510?pid=MOBG9CJ6G5GCFAH4MOBG9CJ6G5GCFAH4LSTMOBG9CJ6G5GCFAH4CCXVXL6 GB RAM | 128 GB ROM 17.22 cm (6.78 inch) Full HD+ Display 108MP + 8MP + 2MP | 32MP Front Camera 6000 mAh Battery Qualcomm Snapdragon 732G Processor 120Hz Refresh Rate Stock Android ExperienceIN_STOCK16999.021999.0INR4.28574687537080275466711950049741
5realme C20 (Cool Grey, 32 GB)2 GB RAMrealmehttps://www.flipkart.com/realme-c20-cool-grey-32-gb/p/itmea1903897436b?pid=MOBGF489SQZCFHYAMOBGF489SQZCFHYALSTMOBGF489SQZCFHYAGB7SDS2 GB RAM | 32 GB ROM | Expandable Upto 256 GB 16.51 cm (6.5 inch) HD+ Display 8MP Rear Camera | 5MP Front Camera 5000 mAh Battery MediaTek Helio G35 ProcessorIN_STOCK7499.07999.0INR4.4260113123881347877191946942373177074
6realme C20 (Cool Blue, 32 GB)2 GB RAMrealmehttps://www.flipkart.com/realme-c20-cool-blue-32-gb/p/itmea1903897436b?pid=MOBGF4894MEWZJGVMOBGF4894MEWZJGVLSTMOBGF4894MEWZJGVW425N52 GB RAM | 32 GB ROM | Expandable Upto 256 GB 16.51 cm (6.5 inch) HD+ Display 8MP Rear Camera | 5MP Front Camera 5000 mAh Battery MediaTek Helio G35 ProcessorIN_STOCK7499.07999.0INR4.4260113123881347877191946942373177074
7POCO C31 (Royal Blue, 32 GB)3 GB RAMPOCOhttps://www.flipkart.com/poco-c31-royal-blue-32-gb/p/itm9656ebd21e0b3?pid=MOBG73E7ZBHHQ3RBMOBG73E7ZBHHQ3RBLSTMOBG73E7ZBHHQ3RBDYSPVO3 GB RAM | 32 GB ROM | Expandable Upto 512 GB 16.59 cm (6.53 inch) HD+ Display 13MP + 2MP + 2MP | 5MP Front Camera 5000 mAh Lithium-ion Polymer Battery MediaTek Helio G35 ProcessorIN_STOCK7999.010999.0INR4.429523170912927032362602919137
8realme 9i (Prism Blue, 64 GB)4 GB RAMrealmehttps://www.flipkart.com/realme-9i-prism-blue-64-gb/p/itm3e9987219f652?pid=MOBG9VGVKUM4XNRRMOBG9VGVKUM4XNRRLSTMOBG9VGVKUM4XNRRJW5YNZ4 GB RAM | 64 GB ROM | Expandable Upto 1 TB 16.76 cm (6.6 inch) Full HD+ Display 50MP + 2MP + 2MP | 16MP Front Camera 5000 mAh Lithium ion Battery Qualcomm Snapdragon 680 (SM6225) ProcessorIN_STOCK12999.015999.0INR4.517977990604266966299213149
9realme 9i (Prism Black, 64 GB)4 GB RAMrealmehttps://www.flipkart.com/realme-9i-prism-black-64-gb/p/itm3e9987219f652?pid=MOBG9VGVYG2XHZGRMOBG9VGVYG2XHZGRLSTMOBG9VGVYG2XHZGRYBJYA74 GB RAM | 64 GB ROM | Expandable Upto 1 TB 16.76 cm (6.6 inch) Full HD+ Display 50MP + 2MP + 2MP | 16MP Front Camera 5000 mAh Lithium ion Battery Qualcomm Snapdragon 680 (SM6225) ProcessorIN_STOCK12999.015999.0INR4.517977990604266966299213149

Last rows

titlerambrandurlproduct_idlisting_idhighlightsavailabilityselling_priceoriginal_pricecurrencyavg_ratingratings_countreviews_countone_stars_counttwo_stars_countthree_stars_countfour_stars_countfive_stars_count
837OPPO F9 (Stellar Purple, 64 GB)4 GB RAMOPPOhttps://www.flipkart.com/oppo-f9-stellar-purple-64-gb/p/itmf8whxzsyhhetm?pid=MOBF8WHXTX8VXSHHMOBF8WHXTX8VXSHHLSTMOBF8WHXTX8VXSHHJD1VQW4 GB RAM | 64 GB ROM | Expandable Upto 256 GB 16.0 cm (6.3 inch) Full HD+ Display 16MP + 2MP | 16MP Front Camera 3500 mAh Battery Mediatek Helio P60 Octacore 2.0 GHz ProcessorCOMING_SOON21990.021990.0INR4.540638442315396642284858027571
838Infinix Note 5 Stylus (Bordeaux Red, 64 GB)4 GB RAMInfinixhttps://www.flipkart.com/infinix-note-5-stylus-bordeaux-red-64-gb/p/itmfb2byf2ghgesf?pid=MOBFB2BVHAGGGBTHMOBFB2BVHAGGGBTHLSTMOBFB2BVHAGGGBTHIKIVXA4 GB RAM | 64 GB ROM | Expandable Upto 128 GB 15.06 cm (5.93 inch) Full HD+ Display 16MP Rear Camera | 16MP Front Camera 4000 mAh Li-ion Polymer Battery Mediatek Helio P23 Octa-core ProcessorCOMING_SOON16999.016999.0INR3.927373833511493135901334
839SAMSUNG Galaxy A50 (Blue, 64 GB)4 GB RAMSAMSUNGhttps://www.flipkart.com/samsung-galaxy-a50-blue-64-gb/p/itmfe4csw5hucdnn?pid=MOBFE4CSF3G2TTUVMOBFE4CSF3G2TTUVLSTMOBFE4CSF3G2TTUV1HA5LO4 GB RAM | 64 GB ROM | Expandable Upto 512 GB 16.26 cm (6.4 inch) Full HD+ Display 25MP + 5MP + 8MP | 25MP Front Camera 4000 mAh Lithium-ion Battery Exynos 9610 Processor Super AMOLED DisplayCOMING_SOON15899.021000.0INR4.36809362553852152745491612342042
840MOTOROLA Edge 20 (Frosted Pearl, 128 GB)8 GB RAMMOTOROLAhttps://www.flipkart.com/motorola-edge-20-frosted-pearl-128-gb/p/itmf7252c5388b02?pid=MOBGYK55HBGMBW25MOBGYK55HBGMBW25LSTMOBGYK55HBGMBW25J2VJML8 GB RAM | 128 GB ROM 17.02 cm (6.7 inch) Full HD+ Display 108MP + 8MP + 16MP | 32MP Front Camera 4000 mAh Battery Qualcomm Snapdragon 778G Processor 144Hz Refresh Rate AMOLED Display 6.99mm Slim DesignIN_STOCK25999.034999.0INR4.1528883156016738011703011
841OPPO A55 (Rainbow Blue, 128 GB)6 GB RAMOPPOhttps://www.flipkart.com/oppo-a55-rainbow-blue-128-gb/p/itmfd07b94a9a543?pid=MOBG84AZHMTVYFBZMOBG84AZHMTVYFBZLSTMOBG84AZHMTVYFBZMOR4TE6 GB RAM | 128 GB ROM | Expandable Upto 256 GB 16.54 cm (6.51 inch) HD+ Display 50MP + 2MP + 2MP | 16MP Front Camera 5000 mAh Lithium-ion Polymer Battery MediaTek Helio G35 ProcessorIN_STOCK17490.020990.0INR4.21358113132682
842OPPO A37f (Black, 16 GB)2 GB RAMOPPOhttps://www.flipkart.com/oppo-a37f-black-16-gb/p/itmewd4kzshreqtm?pid=MOBEWMAUFU9AFWUHMOBEWMAUFU9AFWUHLSTMOBEWMAUFU9AFWUHHNTLTZ2 GB RAM | 16 GB ROM | Expandable Upto 128 GB 12.7 cm (5 inch) HD Display 8MP Rear Camera | 5MP Front Camera 2630 mAh Battery Snapdragon MSM 8916 ProcessorCOMING_SOON9990.010990.0INR4.2115371952891456113424276629
843KARBONN K9 Kavach (Champagne, 16 GB)2 GB RAMKARBONNhttps://www.flipkart.com/karbonn-k9-kavach-champagne-16-gb/p/itmfytcspgzpu4wh?pid=MOBFYJC5CM5MHRMJMOBFYJC5CM5MHRMJLSTMOBFYJC5CM5MHRMJQGWAWV2 GB RAM | 16 GB ROM | Expandable Upto 32 GB 12.7 cm (5 inch) HD Display 5MP Rear Camera 2300 mAh BatteryCOMING_SOON7490.07490.0INR4.128972582941312914511730
844OPPO Reno2 (Ocean Blue, 256 GB)8 GB RAMOPPOhttps://www.flipkart.com/oppo-reno2-ocean-blue-256-gb/p/itmda87752a48a36?pid=MOBFJY8YRNGGZPVDMOBFJY8YRNGGZPVDLSTMOBFJY8YRNGGZPVDUGG2GJ8 GB RAM | 256 GB ROM | Expandable Upto 256 GB 16.51 cm (6.5 inch) Display 48MP + 8MP + 13MP + 2MP | 16MP Front Camera 4000 mAh Battery Qualcomm Snapdragon 730G Processor Ultra Dark Mode l Ultra Steady Video 20x Digital ZoomCOMING_SOON39990.039990.0INR4.41648199106411012791121
845SAMSUNG Galaxy A53 (Light Blue, 128 GB)8 GB RAMSAMSUNGhttps://www.flipkart.com/samsung-galaxy-a53-light-blue-128-gb/p/itm3ceac537b62e9?pid=MOBGCFVYUHHUJFNYMOBGCFVYUHHUJFNYLSTMOBGCFVYUHHUJFNY6GCQII8 GB RAM | 128 GB ROM | Expandable Upto 1 TB 16.51 cm (6.5 inch) Full HD+ Display 64MP + 12MP + 5MP + 5MP | 32MP Front Camera 5000 mAh Lithium Ion Battery Exynos Octa Core Processor ProcessorPRE_ORDER35999.040999.0INR0.00000000
846YU Ace (Rose Gold, 16 GB)2 GB RAMYUhttps://www.flipkart.com/yu-ace-rose-gold-16-gb/p/itmf8hv9kkm5djhu?pid=MOBF8HV9DHJJYXGHMOBF8HV9DHJJYXGHLSTMOBF8HV9DHJJYXGHDFOVEF2 GB RAM | 16 GB ROM | Expandable Upto 128 GB 13.84 cm (5.45 inch) HD+ Display 13MP Rear Camera | 5MP Front Camera 4000 mAh Polymer Battery MT6739WW ProcessorCOMING_SOON7999.07999.0INR4.16610382705079310082281537434322